Hyperlipidemia risk factors among middle-aged population in the United States
Bibliographic record
Abstract
Hyperlipidemia, a major risk factor for cardiovascular disease, disproportionately affects racial and ethnic minority populations. This cross-sectional study examined the prevalence and risk factors for hyperlipidemia among middle-aged adults in the United States using data from the fifth wave of the Adolescent to Adult Health Study (Add Health). The study analyzed merged sociodemographic and biomarker data (N = 4,196) using descriptive statistics and binary logistic regression. The mean age was 37.14 years (SD = ±1.99), with a slightly higher proportion of males (50.38%). The overall prevalence of hyperlipidemia was 16.26%, with higher rates observed in males (20.1%) compared to females. Notably, Asian individuals had significantly higher odds of hyperlipidemia (OR = 2.70, 95% CI: 1.28-5.65), whereas Black/African Americans had a significantly lower risk (OR = 0.57, 95% CI: 0.34-0.94) compared to Whites. Chronic health conditions, including hypertension (OR = 2.46, 95% CI: 1.72-3.52) and diabetes (OR = 4.95, 95% CI: 3.08-7.97), were strong predictors of hyperlipidemia. Additionally, individuals with higher income levels had increased odds of hyperlipidemia (OR = 1.10, 95% CI: 1.01-1.19). Contrary to prior research, obesity was not significantly associated with hyperlipidemia risk. Physical activity was marginally protective, though the effect lost significance in the adjusted model. These findings highlight the importance of targeted cardiovascular health interventions, particularly for Asian populations and those with chronic conditions, to reduce disparities in hyperlipidemia and improve public health outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".